Geospatial Information Systems (GIS) collect, integrate, store, edit, analyze, share, and display geographic information.
Naturally, GIS analysts rely on external data coming from disparate sensors to associate the sensor content (e.g.
imagery) with relational databases. Inherently, these GIS sensors present differences in their data structures, labelling,
ontologies, and resolution. Given different data structures, information may be lost in the transfer of information,
alignment, and association of related context, which yields uncertainty in the meaning of the conveyed information.
Ontology alignment typically consists of manual operations from users with different experiences and understandings
and limited reporting is conducted in the quality of mappings. To assist the International Organization for Standards
(ISO) in development of information quality assessment, we propose an approach using information theory for semantic
uncertainty analysis. Information theory has widely been adopted in communications and provides uncertainty
assessment for quality of service (QOS) analysis. Quality of information (QOI) or Information Quality (IQ) definitions
for semantic assessment can be used to bridge the gap between ontology (semantic) uncertainty alignment and
information theory (symbolic) analysis. Utilizing a measure of semantic information loss, analysts can improve the
information fusion process, predict data needs, and appropriately understand the GIS product. This paper aims at
developing a semantic information loss measure based on information theory relating GIS sensor processing
uncertainties and GIS analyst syntactic associations. A maritime domain situational awareness example with waterway
semantic labels is shown to demonstrate semantic information loss.